• Journal of Navigation and Port Research
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• v.28 no.7
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• pp.635-640
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• 2004

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1863-1866
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• 2002

This paper describes synthesis of a complex R$^{i}$ CR filter with a finite transmission zero except zero frequency. First, a new kernel function is proposed. Secondly, how to determine the element values included in the R$^{i}$ CR filter is described. A fifth-order R$^{i}$ CR filter is designed. Finally, the sensitivity property of the proposed filter is evaluated through computer simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.109-111
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• 2002

Visual organs play an important role in human information recognition processes. If they are expressed in a way of digital information, it makes much bigger amount of visual information among any other information. For that reason, MPEG-2 has been taken use of to represent information compressing technology in multi-media. Although the imported data would basically contain noises, when original video images are encoded into MPET-2. Accordingly, we propose soft- $\alpha$ filter to improve image quality of digital image received from the actual image and to reduce noises from them. We also propose a method combining vertical/horizontal filter and soft- $\alpha$ filter on MPEG-2 video image. We can get two kinds of effects from the advantages of this kind of combination. Firstly, it will reduce processing time ducting horizontal and vetical filtering process. It will cover time for soft- $\alpha$ filter. Secondly, it will simplify the colors in horizontal and vertical filter. Therefore we can get clearer quality without noises from soft- $\alpha$ filter.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2830-2837
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• 1999

Image processing steps are consist of image acquisition, preprocessing, region, segmentation and recognition. But image corrupted commonly by noise reduction methods, many filters were proposed like mean filter, median filter, weighted median filter, Cheikh filter, and Kyu-cheol lee filter as spatial noise reduction filtering. We propose a new edge detection algorithm so that we find out edge existence and nonexistence. In non-edge area, we selectively apply weighted median filter based upon using information of difference value between weighted median filter's value and center pixel's value. As a result, we finally prove a better performance of noise reduction by applying adaptive weighted median filter and improvement of processing time through using simple algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.195-208
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• 2004

Histogram is a very useful method on various practical aspect. With increasing importance of simple calculation method and convenience, it became the basic method in digital image processing nowadays. However, basic limit of using histogram is losing spatial position information of pixels on image. This paper reanalyzes image by presenting histogram with spatial position information(spatial histogram). Also using that result, level order recursive median filter is realized. Presented recursive median filter showed much improved results on edge maintenance aspect compared to existing recursive median filter.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.27-33
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• 2016

In this paper, we propose the bilateral filter based on median value that can reduce random noise and impulse noise with minimal loss of contour information. In general, EO / IR camera to generate a random or impulse noise due to a number of reasons. This noise reduces the performance of detecting and tracking by signal processing. To reduce noise, our proposed bilateral filter sorts the values of the target pixel and the peripheral pixels, and extracts a median filter coefficients of the Gaussian type. Then to extract the Gaussian filter coefficient involved with the distance between the center pixel and the surrounding pixels. As using those filter coefficients, our proposed method can remove the various noise effectively while minimizing the loss of the contour information. To validate our proposed method, we present experimental results for several IR images.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2415-2418
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• 2003

In this paper, we present a new filter to adjust formant information. Spectral envelope in speech analysis shows information about characteristics of speech and formant information determines speech timbre. So, if formant position is adjusted, we can verify adjusted speech timbre. A presented filter is to adjust this formant. This filter is composed of triangular filters. Using this filter we could locate the formant frequency at target position.